Search Results (29)

The consequences of climate change in recent decades include global warming and variations in precipitation patterns. In this research, the impacts of climate change on temperature and precipitation in the east and west of Mazandaran Province, northern Iran, are examined via five GCMs (general circulation models) and two scenarios (SSP2-2.6 and SSP5-8.5) for the baseline period (2005–2023), near future period (2025–2050), and far future period (2051–2080) according to the IPCC (Intergovernmental Panel on Climate Change) Sixth Assessment Report. In the study area, four synoptic stations in the west of Mazandaran and seven stations in the east of Mazandaran are considered. The analyzed data are daily precipitation and minimum, maximum, and average temperatures. Downscaling was performed by using LARS-WG 8.0 (Long Ashton Research Station Weather Generator) software. The results revealed that the SSP5-8.5 (shared socioeconomic pathways) scenario showed better accuracy than the SSP2-2.6 scenario. In the west of Mazandaran, in the near future, the maximum temperature is projected to increase by 1.1 °C, while precipitation is projected to decrease by 26.3 mm, compared to the baseline period. In the east of Mazandaran, in the near future, the maximum temperature is projected to increase by 0.82 °C, while precipitation is expected to decrease by 7.1 mm, compared to the baseline period. In the west of Mazandaran, in the far future, the maximum temperature is projected to increase by 1.34 °C and precipitation is going to decrease by 55.7 mm, relative to the baseline period. In the east of Mazandaran, in the far future, the maximum temperature is projected to increase by 1.1 °C, while precipitation decreases by 31.3 mm, relative to the baseline period. The projected warming trends and precipitation reduction in both the east and west regions of Mazandaran Province are expected to have adverse environmental and socioeconomic implications. Full article

Investigating the spatial-temporal evolutionary trends of future temperature and precipitation considering various emission scenarios is crucial for developing effective responses to climate change. However, researchers in Iraq have not treated this issue under CMIP6 in much detail. This research aims to examine the spatiotemporal characteristics of temperature and rainfall in northern Iraq by applying LARS-WG (8) under CMIP6 general circulation models (GCMs). Five GCMs (ACCESS-ESM1-5, CNRM-CM6-1, MPI-ESM1-2-LR, HadGEM3-GC31-LL, and MRI-ESM2-0) and two emissions scenarios (SSP245 and SSP585) were applied to project the upcoming climate variables for the period from 2021 to 2040. The research relied on satellite data from fifteen weather sites spread over northern Iraq from 1985 to 2015 to calibrate and validate the LARS-WG model. Analysis of spatial-temporal evolutionary trends of future temperature and precipitation compared with the baseline period revealed that seasonal mean temperatures will increase throughout the year for both scenarios. However, the SSP585 scenario reveals the highest increase during autumn when the spatial coverage of class (15–20) °C increased from 27.7 to 96.29%. At the same time, the average seasonal rainfall will rise in all seasons for both scenarios except autumn for the SSP585 scenario. The highest rainfall increment percentage is obtained using the SSP585 for class (120–140) mm during winter. The spatial extent of the class increased from 25.49 to 50.19%. Full article

Climate change and population growth play crucial roles in the planning of future water resources management strategies. In this paper, a balancing between projected water resources and water demands in the Iraqi Part of the Tigris River Basin (TRB) was evaluated till the year 2080 based on RCPs 2.6, 4.5 and 8.5 and population growth. This paper examined a sustainable adaptation plan of water resources in the TRB considering three scenarios; (S1) as no change in the current strategy, (S2) as improved irrigation efficiency and (S3) as improved irrigation and municipal water use efficiency. The results showed a decline in streamflow will occur in the range from 5 to 18.4% under RCP 2.6 and RCP 8.5, respectively. The minimum increase in water demand is expected for RCP 2.6 (maximum increase for RCP 8.5) by 51.8 (208.2), 9.9 (42) and 1.2 (7)% for the municipal–industrial, irrigation and environmental water demands, respectively, compared with the RP. The main finding indicated that S1 is the worst scenario, with water stress in four provinces, especially on the warmest RCP. Whereas, under S2 and S3 conditions, water stress can be eliminated. Increasing ambition towards adaptation becomes obligatory for developing sustainable water sources, supporting water food securities and increasing resilience towards climate change. Full article

Karstic aquifers, because of their conduit system, are susceptible to climate change. Ten karst springs in the Zagros region were selected to investigate the impact of climate change under three CMIP6 scenarios: SSP1-1.9, SSP2-4.5, and SSP5-8.5. This study was conducted in three steps: downscaling climate projection, analyzing spring discharge time series, and introducing a new index to assess the impact of climate change on spring flow rate. Applying LARS-WG6, precipitation was downscaled at 14 stations in the study area. Moreover, time series and trend analysis showed that the selected springs have experienced a decrease in their flow rate. Assuming the covariance function between precipitation and spring discharge is constant, new indices (i.e., $I_{Q_d}$, $I_{{dQ}_d}$, and $I_{cc}$) were introduced to highlight the effect of climate change according to the three scenarios. $d{Q}_d$ is the variability of spring discharge from past to future, $I_{{dQ}_d}$ is spring discharge variability over the historical data, and $I_{cc}$ is the effect of precipitation and spring discharge change together. $I_{cc}$ has a range from −0.25 to 0.25 below and above, which is indicative that two extreme conditions including the spring dryness and overflow are in effect, respectively. The main results revealed that the degree of impact at each spring is a function of climate change scenarios and hydrogeological characteristics of the karstic systems. A more noticeable negative trend in spring flow rate is observed for the karst springs characterized by a dominant conduit flow regime and low matrix storage, located in the areas with low cumulative rainfall, and has a stronger relationship with precipitation. Based on the results, decisions on the management of karst water resources should be made considering where the springs bear free surface and pressurized flow conditions. Full article

This study analyses the impact of climate change on the inflows, sediment loads, and nutrient inputs to the Sabalan dam reservoir, a warm monomictic lake located northwest of Iran. For this purpose, the Soil and Water Assessment Tool (SWAT) was calibrated (2005–2018) and validated (2001–2004). Future climate-based data under the AR5 emission scenarios were obtained from the HadGEM2–ES general circulation model and then downscaled using the LARSWG 6.0. The tuned SWAT model was used to investigate the climate change impact on the hydrological processes and pollution loads to the Sabalan dam reservoir. Our findings based on the Nash–Sutcliffe efficiency coefficient and the coefficient of determination indicated an acceptable performance of the SWAT model in the simulation of inflows, sediment loads, and nutrient inputs to the reservoir. Inflow and sediment load to the reservoir will increase during the period of 2030–2070 compared to the base period (1998–2018). The annual total nitrogen (phosphorus) load to the reservoir will increase by 8.5% (9.4%), 7.3% (8.2%), and 5% (3.4%) under the emission scenarios of RCP2.6, RCP4.5, and RCP8.5, respectively. An increase in sediment loads and nutrient inputs to the Sabalan dam reservoir will significantly exacerbate the reservoir eutrophic condition, leading to water quality deterioration with acute consequences for the positive functions of the dam. Full article

Climate is closely related to human life, food security and ecosystems. Forecasting future climate provides important information for agricultural production, water resources management and so on. In this paper, historical climate data from 1962–2001 was used at three sites in Tianjin Baodi, Tianjin and Tanggu districts as baseline and the model parameters were calibrated by the Long Ashton Research Station Weather Generator (LARS-WG). 2m-temperatures in 2011–2020 were verified under two scenarios, representative concentration pathway (RCP) 4.5 and RCP8.5 in different atmospheric circulation models with optimal minimum 2m-temperatures at the three sites. From 2031–2050, Tianjin will be using more moderate minimum 2m-temperatures in future simulations. Support vector machines (SVM) were used to optimize the simulated data to obtain more accurate future maximum and minimum 2m-temperatures for the three sites. The results showed that the determinant coefficient of LARS-WG simulation was 0.8 and SVM optimized determinant coefficient was 0.9 which greatly improved the prediction accuracy. The minimum and maximum future 2m-temperatures optimized under European Community Earth System Model (EC-EARTH) were relatively low and the same future 2m-temperatures optimized under Hadley Centre Global Environment Model Earth System (Had-GEM2-ES4) were high especially in the RCP8.5 scenario which simulated 2051–2070 climate. The SVM optimization showed that the maximum and minimum 2m-temperatures were in general agreement with the original simulation values. Full article

Futuristic rainfall projections are used in scale and various climate impact assessments. However, the influence of climate variability on spatial distribution patterns and characteristics of rainfall at the local level, especially in semi-arid catchments that are highly variable and are not well explored. In this study, we explore the influence of climate variability on the spatial distribution and rainfall characteristics at a local scale in the semi-arid Shashe catchment, Northeastern Botswana. The LARS-WG, Long Ashton Research Station Weather Generator downscaling method, three representative scenarios (RCP 2.6, RCP 4.5, and RCP 4.5), three trend detection methods (Mann-Kendall, Sen’s slope, and innovative trend analysis) and L-moment method were used to assess climate change impacts on rainfall. Two data sets were used; one with 40 years of observed data from 1981–2020 and the other with 70 years from 1981–2050 (40 years of observed and 30 years of projected data from 2021–2050). Generally, the study found trend inconsistencies for all the trend detection methods. In most cases, Sen’s Slope has a high estimate of observed and RCP 2.6, while ITA overestimates rainfall totals under RCP 4.5 and RCP 8.5. The trend is increasing for annual total rainfall in most gauging stations while decreasing for annual maximum rainfall. The catchment is homogeneous, and Generalized Logistic distribution is the dataset’s best-fit distribution. Spatial coverage of a 100-year rainfall between 151–180 mm will be 81% based on observed data and 87% based on projected data under RCP 2.6 scenario when it happens. A 200-year rainfall between 196–240 mm under RCP 4.5 and 8.5 has high spatial areal coverage, at least 90% of the total catchment. The outcomes of this study will provide insightful information for water resource management and flood risk assessment under climate change. There is a need, however, to assess the transferability of this approach to other catchments in the country and assess the performance of other advanced modelling systems, such as machine learning, in this region. Full article

The contradiction between water demand and water supply in the Yellow River Delta restricts the corn yield in the region. It is of great significance to formulate reasonable irrigation strategies to alleviate regional water use and improve corn yield. Based on typical hydrological years (wet year, normal year, and dry year), this study used the coupling model of AquaCrop, the multi-objective genetic algorithm (NSGA-III), and TOPSIS-Entropy established using the Python language to solve the problem, with the objectives of achieving the minimum irrigation water (IW), maximum yield (Y), maximum irrigation water production rate (IWP), and maximum water use efficiency (WUE). TOPSIS-Entropy was then used to make decisions on the Pareto fronts, seeking the best irrigation decision under the multiple objectives. The results show the following: (1) The AquaCrop-OSPy model accurately simulated the maize growth process in the experimental area. The R² values for canopy coverage (CC) in 2019, 2020, and 2021 were 0.87, 0.90, and 0.92, respectively, and the R² values for the aboveground biomass (BIO) were 0.97, 0.96, and 0.96. (2) Compared with other irrigation treatments, the rainfall in the test area can meet the water demand of the maize growth period in wet years, and net irrigation can significantly reduce IW and increase Y, IWP, and WUE in normal and dry years. (3) Using LARS-WG (a widely employed stochastic weather generator in agricultural climate impact assessment) to generate future climate scenarios externally resulted in a higher CO₂ concentration with increased production and slightly reduced IW demand. (4) Optimizing irrigation strategies is important for allowing decision makers to promote the sustainable utilization of water resources in the study region and increase maize crop yields. Full article

This paper represents a streamflow prediction model with the approach of ensemble multi-GCM downscaling and system dynamics (SD) for the Aji-Chay watershed located in northwest Iran. In this study, firstly, the precipitation and temperature projection for the future was assessed according to the climate change impact using a statistical downscaling technique, i.e., Long Ashton Research Station Weather Generator (LARS-WG); secondly, a rainfall-runoff model for future horizons was developed according to artificial neural networks (ANN); finally, an SD model was developed according to plausible reclamation scenarios, i.e., cloud seeding, increasing the irrigation efficiency and reducing agricultural production, controlling policies on groundwater withdrawal as well as environmental awareness, and cultivation to reduce domestic consumption to achieve sustainable development. For downscaling purposes, the outputs of four general circulation models (GCMs) including EC-EARTH, HadGEM2, MIROC5, MPI-ESM from Coupled Model Intercomparison Project 5 (CMIP5) were applied. The results of multi-GCM downscaling indicated an ascending trend of 0.1 °C to +1.3 °C for temperature and a descending trend of 17 to 23% for precipitation by 2040 under representative concentration pathways (RCPs) of 4.5 and 8.5, respectively. Moreover, the results of the SD model revealed that none of the individual reclamation scenarios were impressive on water balance sustainable conditions; instead, the simultaneous implementation of all plausible scenarios managed to meet the requirements of socio-environment aspects as well as sustainability approaches. Full article

Understanding future changes in water supply and requirement under climate change is of great significance for long-term water resource management and agricultural planning. In this study, daily minimum temperature (T_min), maximum temperature (T_max), solar radiation (Rad), and precipitation for 26 meteorological stations under RCP4.5 and RCP8.5 of MIRCO5 for the future period 2021–2080 were downscaled by the LARS-WG model, daily average relative humidity (RH) was estimated using the method recommended by FAO-56, and reference crop evapotranspiration (ET₀), crop water requirement (ET_c), irrigation water requirement (I_r), effective precipitation (P_e), and coupling degree of ET_c and P_e (CD) for soybean during the growth period were calculated by the CROPWAT model in Heilongjiang Province, China. The spatial and temporal distribution of these variables and meteorological factors were analyzed, and the response of soybean water supply and requirement to climate change was explored. The result showed that the average T_min, T_max, and Rad under RCP4.5 and RCP8.5 increased by 0.2656 and 0.5368 °C, 0.3509 and 0.5897 °C, and 0.0830 and 0.0465 MJ/m², respectively, while the average RH decreased by 0.0920% and 0.0870% per decade from 2021 to 2080. The annual average ET₀, ET_c, P_e, and I_r under RCP4.5 for 2021–2080 were 542.89, 414.35, 354.10, and 102.44 mm, respectively, and they increased by 1.92%, 1.64%, 2.33%, and −2.12% under the RCP8.5, respectively. The ranges of CD under RCP4.5 and RCP8.5 were 0.66–0.95 and 0.66–0.96, respectively, with an average value of 0.84 for 2021–2080. Spatially, the CD showed a general trend of increasing first and then decreasing from west to east. In addition, ET₀, ET_c, and P_e increased by 9.55, 7.16, and 8.77 mm per decade, respectively, under RCP8.5, while I_r decreased by 0.65 mm per decade. Under RCP4.5 and RCP8.5, ET_c, P_e, and I_r showed an overall increasing trend from 2021 to 2080. This study provides a basis for water resources management policy in Heilongjiang Province, China. Full article

Climate change is one of the leading issues affecting river basins due to its direct impacts on the cryosphere and hydrosphere. General circulation models (GCMs) are widely applied tools to assess climate change but the coarse spatial resolution of GCMs limit their direct application for local studies. This study selected five CMIP5 GCMs (CCSM4, HadCM3, GFDL-CM3, MRI-CGCM3 and CanESM2) for performance evaluation ranked by Nash–Sutcliffe coefficient (NSE) and Kling–Gupta Efficiency (KGE). CCSM4 and HadCM3 large-scale predictors were favored based on ranks (0.71 and 0.68, respectively) for statistical downscaling techniques to downscale the climatic indicators T_max, T_min and precipitation. The performance of two downscaling techniques, Statistical Downscaling Methods (SDSM) and Long Ashton Research Station Weather Generator (LARS-WG), were examined using the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), bias, NSE and KGE with weights (W_i) for the validation period. The results of statistical measures proved SDSM more efficient (0.67) in comparison to the LARS-WG (0.51) for the validation time for the Jhelum River basin. The findings revealed that the SDSM simulation for T_max and T_min are more comparable to the reference data for the validation period except simulation of extreme events by precipitation. The 21st century climatic projections exhibited a significant rise in T_max (2.37–4.66 °C), T_min (2.47–4.52 °C) and precipitation (7.4–11.54%) for RCP-4.5 and RCP-8.5, respectively. Overall, the results depicted that winter and pre-monsoon seasons were potentially most affected in terms of warming and precipitation, which has the potential to alter the cryosphere and runoff of the Jhelum River basin. Full article

In terms of having a comprehensive vision toward supplying the water requirements, a multi-criteria decision-making approach was employed on the Zarrine River Basin (ZRB) in the northwest of Iran. First, the climate change impacts were analyzed with the Long Ashton Research Station Weather Generator (LARS-WG) downscaling approach by using General Circulation Models (GCMs) including the European Consortium Earth System Model (EC-EARTH), Hadley Centre Global Environment Model version 2 (HADGEM2), Model for Interdisciplinary Research on Climate, version 5 (MIROC5), and Max Planck Institute Earth System Model (MPI-ESM), from Coupled Model Intercomparison Project 5 (CMIP5) under Representative Concentration Pathway (RCP4.5, RCP8.5) scenarios for 2021–2080. Afterward, the downscaled variables were utilized as inputs to the Artificial Neural Network (ANN) model to predict future runoff under the climate change impact. Finally, the system dynamics (SD) model was employed to simulate various scenarios for assessing water balance utilizing the Vensim software. The results of downscaling models suggested that the temperature of the basin will increase by 0.47 and 0.91 °C under RCPs4.5 and 8.5 by 2040, respectively. Additionally, the precipitation will decrease by 3.5 percent under RCP4.5 and 14 percent under RCP8.5, respectively. Moreover, simulation results revealed that the water demand in various sectors will be enormously increased. The contribution of the climate change impact on the future run-off was a seven percent decrease, on average, over the basin. The SD model, according to presented plausible scenarios including decreasing agriculture product and shifting irrigation efficiency, cloud-seeding, population control, and household consumption reduction, reducing meat and animal-husbandry production, and groundwater consumption control, resulted in a water balance equilibrium over five years. However, the performance of individual scenarios was not effective; instead, a combination of several scenarios led to effective performance in managing reduced runoff under climate change. Full article

This study aimed to assess the spatiotemporal sensitivity of the net irrigation water requirement (NIWR) to changes in climate, for sixteen crops widely cultivated in four irrigation projects located in arid and semi-arid regions of Iraq. Using LARS-WG and five GCMs, the minimum and maximum temperature and precipitation were projected for three periods from 2021–2080 with 20-year steps (P1, P2, and P3) under representative concentration pathways (RCPs) 2.6, 4.5, and 8.5. Weather data available for a reference period from 1990–2019 in four representatives’ meteorological stations were used. The climate variables and other required data were inserted into the CROPWAT 8 NIWR tool. Findings revealed that the increase in the NIWR for the considered crops due to climate change falls in the range 0.1–42.4%, 1.8–44.5%, 1.2–25.1%, and 0.7–14.7% for the North Jazeera Irrigation Project (NJIP), Kirkuk Irrigation Project (KRIP), Upper Khalis Irrigation Project (UKIP), and Dalmaj Irri-gation Project (DLIP), respectively. Barley is more susceptible to changes in climate, whereas maize, potato, soybean, and millet are found to withstand changes in climate better than others. The novel outcomes of this study support optimal spatiotemporal allocation of irrigation water requirement and the sustainable management of water resources in a changing climate in arid and semi-arid regions. Full article

This study evaluated changes in climatic variable impacts on hydrology and water quality in Big Sunflower River Watershed (BSRW), Mississippi. Site-specific future time-series precipitation, temperature, and solar radiation data were generated using a stochastic weather generator LARS-WG model. For the generation of climate scenarios, Representative Concentration Pathways (RCPs), 4.5 and 8.5 of Global Circulation Models (GCMs): Hadley Center Global Environmental Model (HadGEM) and EC-EARTH, for three (2021–2040, 2041–2060 and 2061–2080) future climate periods. Analysis of future climate data based on six ground weather stations located within BSRW showed that the minimum temperature ranged from 11.9 °C to 15.9 °C and the maximum temperature ranged from 23.2 °C to 28.3 °C. Similarly, the average daily rainfall ranged from 3.6 mm to 4.3 mm. Analysis of changes in monthly average maximum/minimum temperature showed that January had the maximum increment and July/August had a minimum increment in monthly average temperature. Similarly, maximum increase in monthly average rainfall was observed during May and maximum decrease was observed during September. The average monthly streamflow, sediment, TN, and TP loads under different climate scenarios varied significantly. The change in average TN and TP loads due to climate change were observed to be very high compared to the change in streamflow and sediment load. The monthly average nutrient load under two different RCP scenarios varied greatly from as low as 63% to as high as 184%, compared to the current monthly nutrient load. The change in hydrology and water quality was mainly attributed to changes in surface temperature, precipitation, and stream flow. This study can be useful in the development and implementation of climate change smart management of agricultural watersheds. Full article

The impacts of future climate changes on watershed hydrochemical processes were assessed based on the newest Shared Socioeconomic Pathways (SSP) scenarios in Coupled Model Intercomparison Project Phase 6 (CMIP6) in the Tianhe River in the middle area of China. The monthly spatial downscaled outputs of General Circulation Models (GCMs) were used, and a new Python procedure was developed to batch pick up site-scale climate change information. A combined modeling approach was proposed to estimate the responses of the streamflow and Total Dissolved Nitrogen (TDN) fluxes to four climate change scenarios during four future periods. The Long Ashton Research Station Weather Generator (LARS-WG) was used to generate synthetic daily weather series, which were further used in the Regional Nutrient Management (ReNuMa) model for scenario analyses of watershed hydrochemical process responses. The results showed that there would be 2–3% decreases in annual streamflow by the end of this century for most scenarios except SSP 1-26. More streamflow is expected in the summer months, responding to most climate change scenarios. The annual TDN fluxes would continue to increase in the future under the uncontrolled climate scenarios, with more non-point source contributions during the high-flow periods in the summer. The intensities of the TDN flux increasing under the emission-controlled climate scenarios would be relatively moderate, with a turning point around the 2070s, indicating that positive climate policies could be effective for mitigating the impacts of future climate changes on watershed hydrochemical processes. Full article